In silico simulation with GAMESS-UK

On HPCx, Dr
Marcus Durrant of the John
Innes Centre is carrying out in silico simulation of
molecular evolution, by means of quantum calculations. In molecular
evolution, genes are transcribed into functional molecules (proteins),
which then carry out specific chemical processes. The survival of a
gene is therefore determined primarily by the ability of its
associated protein to carry out its target chemical reaction. In this
project, we are using very small genes (nanogenes) to code for
specific molecules, namely transition metal complexes. Individual
nanogenes are transcribed to give sets of input files for quantum
calculations using the GAMESS-UK
quantum chemistry package. Survival of individual nanogenes within
the population depends upon the ability of each complex to perform a
specified chemical reaction, assessed by the results of the quantum
calculations. Surviving nanogenes are allowed to breed and mutate in
order to provide the next generation, and the process is repeated
until all the survivors are fully competent for the target reaction.

To date, we have completed a pilot study, which has successfully
demonstrated the basic concept. This pilot study consisted of a total
of 18 generations, comprising 289 individual nanogenes. Of these, 17
nanogenes that satisfied the selection criteria were found.
Interestingly, these were all closely related to an experimentally
characterised complex, which has been shown to carry out the target
reaction. Hence, the pilot study has demonstrated a number of key
points:

Population size, diversity, and variable selection pressures are key
factors in driving the evolutionary process.

The method can identify lead molecules for a particular function
without any prior chemical knowledge apart from a clear definition of
the problem.

We are currently extending our studies to search for a catalyst capable
of reducing dinitrogen to hydrazine.

Caption for figure: Three successive generations of
nanogenes, showing the spread of desirable characteristics through a
population. In each case, the top six nanogenes are chosen as
survivors to form the next generation through breeding and mutation
algorithms.